Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient...

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Detalles Bibliográficos
Autores principales: Phan, Nam Nhut, Huang, Chi-Cheng, Tseng, Ling-Ming, Chuang, Eric Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673486/
https://www.ncbi.nlm.nih.gov/pubmed/34926274
http://dx.doi.org/10.3389/fonc.2021.769447

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673486/
https://www.ncbi.nlm.nih.gov/pubmed/34926274
http://dx.doi.org/10.3389/fonc.2021.769447

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